Update app.py

app.py

@@ -1,73 +1,26 @@
-import gradio as gr
-from langchain.document_loaders import OnlinePDFLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.llms import HuggingFaceHub
-from langchain.embeddings import HuggingFaceHubEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.chains import RetrievalQA
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer
+import torch
 
-def loading_pdf(): return 'Loading...'
+#dataset_name = "timdettmers/openassistant-guanaco" ###Human ,.,,,,,, ###Assistant
 
-def pdf_changes(pdf_doc, repo_id):
-    loader = OnlinePDFLoader(pdf_doc.name)
-    documents = loader.load()
-    text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=0)
-    texts = text_splitter.split_documents(documents)
-    embeddings = HuggingFaceHubEmbeddings()
-    db = Chroma.from_documents(texts, embeddings)
-    retriever = db.as_retriever()
-    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={'temperature': 0.5, 'max_new_tokens': 2096})
-    global qa 
-    qa = RetrievalQA.from_chain_type(llm=llm, chain_type='stuff', retriever=retriever, return_source_documents=True)
-    return "Ready"
+dataset_name = 'AlexanderDoria/novel17_test' #french novels
+dataset = load_dataset(dataset_name, split="train")
+demo.launch()
 
-def add_text(history, text):
-    history = history + [(text, None)]
-    return history, ''
 
-def bot(history):
-    response = infer(history[-1][0])
-    history[-1][1] = response['result']
-    return history
 
-def infer(question):
-    query = question
-    result = qa({'query': query})
-    return result
+model_name = "TinyPixel/Llama-2-7B-bf16-sharded"
 
-css="""
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-"""
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
 
-title = """
-    <h1>Chat with PDF</h1>
-"""
-
-with gr.Blocks(css=css, theme='NoCrypt/[email protected]') as demo:
-    with gr.Column(elem_id='col-container'):
-        gr.HTML(title)
-
-    with gr.Column():
-        pdf_doc = gr.File(label='Upload a PDF', file_types=['.pdf'])
-        repo_id = gr.Dropdown(label='LLM', 
-                              choices=[
-                                  'mistralai/Mistral-7B-Instruct-v0.1', 
-                                  'HuggingFaceH4/zephyr-7b-beta', 
-                                  'meta-llama/Llama-2-7b-chat-hf', 
-                                  '01-ai/Yi-6B-200K'
-                              ],
-                             value='mistralai/Mistral-7B-Instruct-v0.1')
-        with gr.Row():
-            langchain_status = gr.Textbox(label='Status', placeholder='', interactive=False)
-            load_pdf = gr.Button('Load PDF to LangChain')
-
-        chatbot = gr.Chatbot([], elem_id='chatbot')#.style(height=350)
-        question = gr.Textbox(label='Question', placeholder='Type your query')
-        submit_btn = gr.Button('Send')
-
-    repo_id.change(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False)
-    load_pdf.click(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False)
-    question.submit(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot)
-    submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot)
-
-demo.launch()
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    trust_remote_code=True
+)
+model.config.use_cache = False